Modifiable and non-modifiable risk factors for COVID-19: results … · 2020. 4. 28. · 1.38),...

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Modifiable and non-modifiable risk factors for COVID-19: results from UK Biobank

Frederick K Ho*1, Carlos A Celis-Morales*1,2, Stuart R Gray, S Vittal Katikireddi1, Claire L

Niedzwiedz1, Claire Hastie1, Donald M Lyall1, Lyn D Ferguson2, Colin Berry2, Daniel F

Mackay1, Jason MR Gill2, Jill P Pell**1, Naveed Sattar MD**2, Paul Welsh**2

*Joint first authors

**Joint senior

Affiliations

1 Institute of Health and Wellbeing, University of Glasgow, 1 Lilybank Gardens, Glasgow,

G12 8RZ, United Kingdom

2 Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF Glasgow

Cardiovascular Research Centre, 126 University Place, Glasgow, G12 8TA, United Kingdom

Address for correspondence

Paul Welsh OR Naveed Sattar

Institute of Cardiovascular and Medical Sciences,

University of Glasgow,

BHF Glasgow Cardiovascular Research Centre,

126 University Place, Glasgow G12 8TA, UK.

Tel: +44 (0)141 330 1722

E-mail address: [email protected]

All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20083295doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

mailto:[email protected]

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Abstract

Background

Information on risk factors for COVID-19 is sub-optimal. We investigated demographic,

lifestyle, socioeconomic, and clinical risk factors, and compared them to risk factors for

pneumonia and influenza in UK Biobank.

Methods

UK Biobank recruited 37-70 year olds in 2006-2010 from the general population. The

outcome of confirmed COVID-19 infection (positive SARS-CoV-2 test) was linked to baseline

UK Biobank data. Incident influenza and pneumonia were obtained from primary care data.

Poisson regression was used to study the association of exposure variables with outcomes.

Findings

Among 428,225 participants, 340 had confirmed COVID-19. After multivariable adjustment,

modifiable risk factors were higher body mass index (RR 1.24 per SD increase), smoking (RR

1.38), slow walking pace as a proxy for physical fitness (RR 1.66) and use of blood pressure

medications as a proxy for hypertension (RR 1.40). Non-modifiable risk factors included

older age (RR 1.10 per 5 years), male sex (RR 1.64), black ethnicity (RR 1.86), socioeconomic

deprivation (RR 1.26 per SD increase in Townsend Index), longstanding illness (RR 1.38) and

high cystatin C (RR 1.24 per 1 SD increase). The risk factors overlapped with pneumonia

somewhat; less so for influenza. The associations with modifiable risk factors were generally

stronger for COVID-19, than pneumonia or influenza.

Interpretation

These findings suggest that modification of lifestyle may help to reduce the risk of COVID-19

and could be a useful adjunct to other interventions, such as social distancing and shielding

of high risk.

Funding

British Heart Foundation, Medical Research Council, Chief Scientist Office.



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Introduction

COVID-19, caused by SARS-CoV-2 infection, includes a spectrum of morbidity from

asymptomatic infection 1 to severe pneumonia in patients presenting for medical care 2.

The COVID-19 pandemic 3 has led to concerted research efforts to identify people at

greatest risk of developing the infection and progressing to critical illness and dying.

Predictors of disease severity include older age, smoking, diabetes, hypertension, kidney

disease, COPD, and previous cardiovascular disease 4–7. In addition it is becoming clear that

other risk factors might include obesity and low physical fitness 8,9. However, many studies

investigate risk factors for disease progression to death or critical illness among hospitalised

patients7 as opposed to healthy comparators.

Identifying the risk factors for COVID-19 is important in terms of identifying factors that can

be modified to reduce risk, as well as identifying non-modifiable risk factors that can help

identify high risk groups who require shielding and targeting for testing, and eventual

vaccine and anti-viral therapies. Pneumonia is a life-threatening complication of COVID-19

infection 10. Established major risk factors for community-acquired pneumonia include many

of the emerging risk factors for COVID-19 11. As COVID-19 is caused by SARS-CoV-2 viral

infection, it may have risk factors for contraction of the disease that are common to other

respiratory virus conditions such as Influenza.

UK Biobank is a large prospective, deeply-phenotyped, population-based cohort study

carried out in the UK 12. Over the study period, testing for COVID-19 in England was

conducted in A&E departments and in-hospital. These data were provided by Public Health

England (PHE) and linked to UK Biobank baseline data. We aimed to establish modifiable

and non-modifiable risk factors for confirmed COVID-19. We also aimed to compare these

risk factors to risk factors for the incident pneumonia over a similar time span.

Methods

UK Biobank was conducted via 22 assessment centres across England, Scotland, and Wales

between March 2006 and December 2010 and recruited 502,624 participants aged 37 to 73



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years. The present study was restricted to participants living in England for whom COVID-19

test results were available. Death data were available to the end of January 2018 in England.

To reduce bias, we excluded from the study all participants known to have died COVID-19

pandemic. Baseline biological measurements were recorded and touch-screen

questionnaires were administered according to a standardised protocol. 12,13 UK Biobank

received ethical approval from the North West Multi-Centre Research Ethics Committee

(REC reference: 11/NW/03820). All participants gave written informed consent before

enrolment in the study, which was conducted in accord with the principles of the

Declaration of Helsinki. This study was performed under UK Biobank application number

7155.

Outcomes

Results of COVID-19 tests for UK Biobank participants were provided by PHE 7. Data

provided by PHE included the specimen date, specimen type (e.g. upper respiratory tract),

laboratory, origin (whether evidence from microbiological record that the participant was

an inpatient or not) and result (positive or negative). Data were available for the period 16th

March 2020 to 14th April 2020. Results were available for 2,724 tests conducted on 1,474

individuals. Confirmed COVID-19 infection (primary outcome) was defined as at least one

positive result in the context of an in-hospital or accident and emergency (A&E) test. Any

positive test result was used as a sensitivity analysis.

Pneumonia was defined based on ICD-10 codes J12-J18, and influenza based on J09-J11,

converted into Read Codes using the UK Biobank’s look-up table. Incident pneumonia and

influenza, occurring after 1st January 2016 (an arbitrary date taken to mimic the time lag

from baseline exposure measurement to incident COVID-19 infection, while also obtaining

sufficient case numbers) was obtained from a 41% sample of participants with available

data from primary care. A sensitivity analysis was conducted using cases after 1st January

2015 and demonstrated consistent data. An analysis was also conducted exploring risk

factors for all cases of pneumonia and influenza after baseline.

Exposures



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Exposures were measured at the baseline assessment visit between 2006 and 2010.

Ethnicity, smoking, alcohol consumption, physician-diagnosed prevalent conditions and

medication use were self-reported. For the present analyses, ethnicity was coded as white,

south Asian, black, or mixed/other. Smoking status was categorised into never versus

former/current smoking. Systolic and diastolic blood pressures were measured at the

baseline visit, preferentially using an automated measurement, but using manual

measurement where this was not available, and average of available measures used. Area-

level socioeconomic deprivation was assessed by the Townsend score (incorporating

measures of unemployment, non-car ownership, non-home ownership and household

overcrowding) corresponding to the participants’ home postcode. Higher scores on the

Townsend score represent greater socioeconomic deprivation. Self-reported walking pace

was rated by each participant as slow, steady/average, or brisk 14.

The definition of baseline diabetes included self-reported type 1 or type 2 diabetes, those

with a primary or secondary hospital diagnoses relating to diabetes at baseline (ICD-10

codes E10-E14.9), and those who reported using diabetes medications. Baseline

cardiovascular disease was defined as self-reported myocardial infarction, stroke, or

transient ischaemic attack. Cancer, longstanding illness, and depression were self-reported

on touchscreen questionnaire. Other previous health complaints including asthma,

rheumatoid arthritis, CKD, systemic lupus erythematosus (SLE), sleep apnoea, COPD,

pneumonia, bronchitis (including bronchitis, bronchiectasis, and emphysema), and other

respiratory diseases (including interstitial lung disease, asbestosis, pulmonary fibrosis,

alveolitis, respiratory failure, pleurisy, pneumothorax, other respiratory condition) which

were derived from self-report at nurse interview. Some conditions did not have sufficient

numbers of cases for multivariable analysis, but univariable results are presented for

completeness. Statin (categorised to include other cholesterol lowering medications) and

blood pressure medication use were also recorded from self-report, with blood pressure

medication being used as a proxy for baseline diagnosed hypertension.

BMI is the ratio of the measured body mass in kg divided by height squared measured in

metres. Height was measured using a Seca 202 height measure. Weight and whole-body fat

mass and fat free mass were measured to the nearest 0.1 kg using the Tanita BC-418 MA



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body composition analyser. Socks and shoes were removed when height was measured

Grip strength was measured using a Jamar J00105 hydraulic hand dynamometer and the

mean was derived from the right and left hand values expressed in kilograms 15.

Lung function was assessed by spirometry using a Vitalograph Pneumotrac 6800 spirometer

(Vitalograph, Buckingham, UK). Participants did not perform spirometry if they answered

yes to unsure to the following: chest infection in the last month, history of detached retina,

heart attack, surgery to eyes, chest or abdomen in last 3 months, history of collapsed lung,

pregnancy, or currently on medication for tuberculosis. The aim was to record two

acceptable blows from a maximum of three attempts. The spirometer software compared

the acceptability of the first two blows and, if acceptable (defined as a ≤5% difference in FVC

and FEV1), the third blow was not required. The mean observation was taken for both

measures.

Blood collection sampling procedures for the study have been previously described and

validated.16 Biochemical assays were performed at a dedicated central laboratory on around

480,000 samples. Further details of these measurements can be found in the UK Biobank

Data Showcase and Protocol (http://www.ukbiobank. ac.uk). For the present study we

included total cholesterol, HDL cholesterol, rheumatoid factor, cystatin C, glycated

haemoglobin (HbA1C), C-reactive protein (CRP), differential white blood cell count, and red

cell distribution width as exposures of interest.

Statistics

Mean and standard deviations were reported for continuous outcomes except for

biomarkers, where median and interquartile ranges (IQR) were reported. Poisson regression

with robust ‘sandwich’ standard errors were used to study the associations of exposure

variables with confirmed COVID-19 and pneumonia. Poisson regressions were used because

they provide risk ratio (RR) which is easier to interpret and robust error estimation ensures

accurate inference 17. Three adjustment schemes were considered: model 0 - univariate (i.e.

no adjustment), model 1 – adjusted for age, sex, ethnicity, and deprivation index, and model

2 – further adjusted for behavioural (smoking and alcohol drinking) and physical (adiposity,

blood pressure, spirometry, and physical capability) factors that were found to be significant



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in model 1. To avoid multicollinearity, if there were similar factors that were significant in

model 2, only the one with strongest association was chosen. This included, for example,

choosing one from, BMI, BMI categories, body fat-free mass, body fat mass, and body fat

percent, or one from FEV1, FVC, and FEV1/FVC. For continuous variables, the linearity of

exposure-outcome associations were tested using penalised cubic splines in generalised

additive model (GAM) 18. Nonlinearity was tested using likelihood ratio test comparing a

model with the exposure fitted on a spline with a model assuming a linear exposure-

outcome relationship. P-value for nonlinearity < 0.05 suggest evidence against the linearity

assumption. Spline smoothness was chosen using generalised cross validation 19. Population

attributable fractions (PAFs) were calculated to determine the relative contribution of each

risk factor to the overall number of confirmed COVID-19 cases within UK Biobank. Another

Poisson model which included all significant factors in model 2 was fitted to estimate the

mutually adjusted RRs. These RRs were biased towards null because of over adjustment bias

but were used to construct the PAFs to ensure the PAF estimates did not overlap or exceed

100%. In general, two-tailed P-values < 0.05 were considered statistically significant.

Analyses were conducted in R Statistical Software version 3.5.3 with the package “mgcv”.

Results

Of 445,857 participants in England, 428,225 were alive during the available follow-up

period. Complete data on covariates were available for 285,817 (66.7%) participants.

Primary care data on incident pneumonia were available in 117,948 (41.3%) participants

(Supplementary Fig 1). At 1st March 2020, the age range of eligible participants was 48-85

years, and time elapsed from baseline was median 10.97 years (IQR 10.36-11.55 years). Of

these participants, 898 received at least one COVID-19 test, and 400 had at least one

positive result, with 340 positive results conducted in hospital or A&E (primary outcome).

Univariable risk factors for incident COVID-19

Key univariable potentially modifiable risk factors for confirmed COVID-19 included current

and former smoking (RR 1.62), higher BMI and body fat (RR 2.29 for obesity), treated

hypertension (RR 2.07), higher systolic and diastolic blood pressure, and slow walking pace

as a proxy for fitness (RR 2.40) (Table 1). Among non-modifiable risk factors were older age



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(particularly the over 65s at baseline, corresponding to over 75 in 2020), male sex (RR 1.56),

black ethnicity (RR 2.74), socioeconomic deprivation (RR 1.37 per 1 SD increase in Townsend

Index) (Table 1). COVID-19 was only weakly associated with low FEV1/FVC ratio (RR 0.90 per

1 SD increase). Among baseline comorbidities, general long standing illness (RR 1.88),

baseline diabetes (RR 2.07), baseline CVD (RR 2.19), sleep apnoea (RR 3.91), statin use (RR

1.85), higher inflammatory markers (particularly white blood cell count; RR 1.20 per 1 SD

increase), and higher cystatin C (RR 1.45 per 1 SD increase) were also associated with

confirmed COVID-19 (Table 1). Risk factor associations were similar when all 400 test

positive COVID-19 cases were considered, rather than only in-hospital positive tests

(supplementary table 1)

Univariable risk factors for incident pneumonia

Of the 117,837 participants with primary care data, 259 had pneumonia recorded after

2016. Of the modifiable risk factors pneumonia was associated with BMI and slow walking

pace, but not smoking, blood pressure or blood pressure medication use. Incident

pneumonia was more common in participants who were over 55 years at baseline, women

(RR 0.61 among men), and socioeconomically deprived participants (RR 1.37 per 1 SD

increase in Townsend Index), (Table 1). Pneumonia was also more common in those with

low grip strength, and lower FEV and FVC, and FEV/FVC ratio (Table 1). Among

comorbidities, baseline diabetes, CVD and cancer were approximately twice as common in

those who developed pneumonia (Table 1), and any longstanding illness was also strong

associated with pneumonia. Among blood biomarkers, poorer renal function as measured

by higher cystatin-C, higher HbA1C, higher CRP and white blood cell count (including

neutrophils, monocytes, and lymphocytes) and higher red cell distribution width were all

associated with pneumonia. Ethnicity was not associated with pneumonia.

When investigating risk factors for pneumonia over the full follow-up time from baseline,

risk factors were generally similar although there was increased power due to a higher

number of incident cases (Supplementary table 1). In this analysis smoking was weakly

associated with pneumonia (RR 1.17) as was blood pressure medication use (RR 1.15).

Similar findings were found when we studied pneumonia occurring after 2015

(Supplementary Table 2).



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Univariable risk factors for incident influenza

Of the 117,837 participants with primary care data, 111 had influenza recorded after 2016.

Those who developed influenza were generally more socioeconomically deprived, more

likely to have bronchitis at baseline, and less likely to take statins. No other risk factors

showed strong associations (Table 1). When all influenza cases occurring after baseline were

considered, evidence of statistically significant associations became stronger due to

increased power but cases were more common in slightly younger people and in women,

and smoking, walking pace, and BMI was less strongly associated with influenza cases than

for COVID-19, and blood pressure medication was not at all associated with influenza

(Supplementary Table 1).

Multivariable models for COVID-19 and pneumonia

After multivariable adjustment for sociodemographic factors, the modifiable risk factors for

confirmed COVID-19 included smoking (RR 1.44 (95% CI 1.15-1.79)), higher BMI (RR 1.33 per

SD increase (95% CI 1.21-1.46)) and other measures of body fat, diastolic blood pressure, as

well as blood pressure medication, FEV1 and FVC (but not FEV1/FVC ratio), and slow walking

pace (RR 2.04 (95% CI 1.49-2.79)) (Table 2). Other risk factors were older age (RR 1.15 per 5

years (95% CI 1.08-1.24)), male sex (RR 1.54 (95% CI 1.24-1.91)), black ethnicity (RR 2.07

(95% CI 1.16, 3.71)), socioeconomic deprivation (RR 1.35 per SD increase in Townsend Index

(95% CI 1.23-1.49)). Among comorbidities, risk factors included longstanding illness (RR 1.65

(95% CI 1.32-2.06)), diabetes, CVD and statin use. Among blood biomarkers, risk factors

included lower total and HDL-cholesterol, and higher cystatin C (RR 1.36 per 1 SD increase

(95% CI 1.23-1.49)), HbA1C (RR 1.20 per 1 SD increase (95% CI 1.11-1.30)), CRP and white

blood cell count (Table 2).

After adding BMI, blood pressure, FEV1 and walking pace as covariates, modifiable risk

factors for COVID-19 admission continued to include smoking (RR 1.38 (95% CI 1.11-1.73)),

higher BMI (RR 1.24 (95% CI 1.12-1.38)), use of blood pressure lowering medication as a

proxy for hypertension (RR 1.40 95% CI 1.08-1.82) and slow walking pace (RR 1.66 (95% CI

1.20-2.30)). Other risk factors included older age (RR 1.10 per 5 year increase (95% CI 1.02-

1.36)), male sex (RR 1.64 (95% CI 1.25-2.14)), black ethnicity (RR 1.86 (95% CI 1.03, 3.36)),



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socioeconomic deprivation (RR 1.26 (95% CI 1.14-1.40)), longstanding illness (RR 1.38 (95%

CI 1.09-1.74)), lower HDL-cholesterol (RR 0.82 (95% CI 1.72-0.95)) and higher cystatin C (RR

1.24 (95% CI 1.12-1.37)) (Table 2).

Nonlinear associations of continuous variables with COVID-19 and pneumonia are shown in

Figure 1 (and supplementary table 2). Age was associated with COVID-19 admission

curvilinearly, where participants aged 40-60 years shared similar risk and participants aged

over 60 years had exponentially increased risk with age. Associations were similar when

adjusting for body fat percentage rather than BMI (Supplementary Table 3).

Directly contrasting model 2 for COVID-19 and pneumonia (Figure 2), the risk factors in

common for both conditions were older age, higher BMI and slow walking pace, although

the latter two associations less strongly associated with pneumonia than for COVID-19.

Highlighting specific differences, smoking and treated blood pressure were only associated

with COVID-19. Pneumonia was more common in women, and socioeconomic deprivation

and cystatin-C were not associated with pneumonia. Low FEV1 was independently

associated with pneumonia, but not with COVID-19. Pneumonia also showed an association

with baseline cancer.

Population attributable risks

Factors that were significant in Table 2 were mutually adjusted to compare their population

attributable fractions for COVID-19 and pneumonia (Table 3). Among potentially modifiable

risk factors smoking accounted for 12.8% of COVID-19 cases that occurred within the UK

Biobank population, treated BP with 5.0% of cases, slow walking pace with 2.8%, and BMI

with 0.6% (total 21.2%). In contrast, none of these factors were large contributors to

pneumonia cases within UK Biobank. (Table 3)

Discussion

In this population-based study, we found that confirmed COVID-19 infection was associated

with a number of modifiable risk factors, a trend that less apparent for pneumonia and



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influenza. In particular, the associations of smoking, BMI (and body fat), hypertension, and

physical fitness (as measured by slow walking pace) with COVID-19 are of note; even when

such factors were measured a decade before infection they potentially accounted for one-

fifth of UK Biobank confirmed COVID-19 cases. The other independent risk factors for

COVID-19 infection included older age, male sex, black ethnicity, socioeconomic deprivation,

longstanding illness, and reduced renal function as measured by cystatin C, the latter also

notable given renal complication in severe COVID-19.

It is importance to recognise the competing risk for health of lockdown and social

distancing. Social distancing reduces viral transmission, but also has consequences for

lifestyle. Previous data suggests that physical fitness can be rapidly lost when activity levels

decrease 20,21, and this will also result in an increase in BMI 22,23. Further, social distancing

may increase loneliness, depression, and psychological stress in some people and

consequently adversely affect eating habits 24 and other health behaviours. Anecdotal

evidence from our clinics and media suggest many are struggling with overeating. This study

strongly suggests public health guidance should focus on reducing the risk of severe

complications of COVID-19 by advocating a healthy lifestyle during the ongoing pandemic,

not just for general cardiovascular and metabolic health, but also to help to protect against

COVID-19 infection, used alongside other public health interventions.

Understanding the actual causes of disease, rather than markers that simply correlate with

exposures, is clearly a key issue to consider. The independent association of socioeconomic

deprivation with COVID-19 after multiple adjustment may be explained by the accumulation

of earlier life socioeconomic adversities which can result in less physiological reserve and

more multimorbidity 24,25. It may also be linked to more overcrowding, reduced social

distancing, and potential exposure to greater viral load. Asthma, diabetes and high blood

pressure, which have been shown to be associated with a higher risk of severe COVID-19

outcomes 26 also showed some trends to be associated with COVID-19 in this dataset. While

substantial focus of COVID-19 research has been its apparently more aggressive symptoms

and disease progression in older people, it is important to recognise that people who are

older have less cardiorespiratory reserve to cope with COVID-19 infection. Older age is also



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associated with more hypertension and diabetes, poorer lung function, and greater relative

fat mass 24.

Previous reports have suggested that obesity or excess ectopic fat deposition may be a

unifying risk factor for severe Covid-19 infection 27,28, reducing both protective

cardiorespiratory reserve as well as potentiating the immune dysregulation that appears, at

least in part, to mediate the progression to critical illness in a proportion of COVID-19

patients. Our analysis bears out these hypotheses. Once BMI or body fat was adjusted for,

inflammatory markers were no longer associated with incident COVID-19. This suggests

proinflammatory markers, arising from increased adipose deposition, are probably acting as

a marker for body fat which seems to be an adverse risk factor for severe COVID-19 27–30.

Furthermore, obesity enhances thrombosis 31, which is relevant given the association

between severe COVID-19 and pro-thrombotic disseminated intravascular coagulation and

high rates of venous thromboembolism 32,33, as well as the association with D-dimer seen in

other reports 34. D-dimer was not measured in blood samples in UK Biobank.

Given that pneumonia is a critical clinical complication of COVID-19, it is important to

understand how risk factors for COVID-19 related pneumonia differ from “classic”

community acquired pneumonia 11. Our comparison between other common respiratory

diseases and COVID-19 suggests that identification of at-risk groups based on our

understanding of other respiratory diseases may be inadequate – a more refined approach

to risk stratification based on COVID-19 specific risks is needed, and future data should focus

on some of the exposures we identify to achieve this.

The strengths of our study include the large cohort size at an age relevant to more severe

COVID-19 symptoms, and biochemistry assays performed in a single dedicated central

laboratory. We were also able to extensively explore emerging and novel risk factors for

COVID-19, while simultaneously comparing to risk factors for pneumonia identified to

primary care providers. Limitations include that UK Biobank is not representative of the

whole UK population,35 (our data focuses on England specifically) although this is generally

not a concern in investigating risk associations36. Care should be taken in generalising the

PAF estimates. These related to cases occurring within the UK Biobank population, but are



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not directly applicable to the general population where the prevalence of risk factors is

different. However, they allowed a direct comparison between COVID-19 and pneumonia in

the same cohort. Due to the under-representation of non-white ethnicities in UK Biobank,

we have limited power to explore important interactions by ethnic group (although Black

ethnicity was associated with the outcome), and we recognise this as an important risk

factor. Ascertainment bias, including differential healthcare seeking, differential testing and

differential prognosis may explain some differences in outcomes given poor coverage of

testing in the UK. It is also likely that cases will generally be at the more severe end of the

clinical spectrum by using hospitalised cases as the outcome, although use of admission to

ITU would be additionally informative once sufficient case numbers accrue. Despite this, we

still observe many similar risk factors to incident pneumonia, which is more likely to have

close to complete case ascertainment in primary care records, although, as discussed, other

risk factors seem more strongly linked to COVID-19 infections. Exposures were measured

several years before the development of the outcomes, and misclassification of the

exposures will likely bias our results to the null. However, this also serves to illustrate that

the risk factors were generally present many years before development of the disease, and

as is well known, risk factors tend to track with age. We have also been unable to fully

exclude all deaths that occurred prior to the pandemic, due to lack of up-to-date linkage to

mortality records.

In conclusion, these early data from UK Biobank suggest risk factors for confirmed COVID-19

infection differ in some important ways from risk factors for pneumonia, being more

common in males than females, in lower SES, and with stronger associations with ethnicity,

CV risk markers, prior smoking and adiposity. Such findings suggest possible merit in

advocating improvements in lifestyle as an additional measure to reduce the risk of COVID-

19 alongside existing public health measures such as social distancing and shielding of high

risk groups. have implications for health advice targeted at the public to lessen risks during

this pandemic

Contributors

PW, JPP and NS conceived the idea for the paper. FH conducted the analysis. All authors

contributed to the interpretation of the findings. PW, FH, CCM and NS jointly wrote the first



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draft. All authors critically revised the paper for intellectual content and approved the final

version of the manuscript.

Declarations of interest:

JPP is a member of the UK Biobank Steering Committee. Apart from the funding

acknowledged below, we declare no other competing interests.

Acknowledgements

The work in this study is supported by the British Heart Foundation Centre of Research

Excellence Grant RE/18/6/34217. CLN acknowledges funding from a Medical Research

Council Fellowship (MR/R024774/1). SVK acknowledge funding from the Medical Research

Council (MC_UU_12017/13) and Scottish Government Chief Scientist Office (SPHSU13). SVK

also acknowledges funding from NRS Senior Clinical Fellowship (SCAF/15/02).



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Table 1. Univariable association of baseline risk factors with COVID-19 in 2020, pneumonia occurring after 2016, and influenza occurring after 2016

Covid-19 Pneumonia (after 2016) Influenza (after 2016)

No

n=285477 Yes

n=340 RR* P No

n=117689 Yes

n=259 RR P No

n=117837 Yes

n=111 RR P

Age (years) 56.15 (8.12) 57.66 (8.49) 1.21† 0.0006 56.23 (8.09) 58.29 (7.57) 1.31 < 0.0001 56.24 (8.09) 56.11 (7.99) 0.98 0.87

Age categories (years), n (%) < 0.0001 0.0005 0.41

<50 71218 (24.95) 76 (22.35) 1 REF 28967 (24.61) 40 (15.44) 1 REF 28983 (24.60) 24 (21.62) 1 REF

50-54 44697 (15.66) 41 (12.06) 0.86 0.44 18257 (15.51) 29 (11.20) 1.15 0.57 18262 (15.50) 24 (21.62) 1.59 0.11

55-59 51240 (17.95) 51 (15.00) 0.93 0.70 21184 (18.00) 58 (22.39) 1.98 0.0009 21220 (18.01) 22 (19.82) 1.25 0.45

60-64 67559 (23.67) 78 (22.94) 1.08 0.63 28256 (24.01) 79 (30.50) 2.02 0.0003 28312 (24.03) 23 (20.72) 0.98 0.95

≥65 50763 (17.78) 94 (27.65) 1.73 0.0004 21025 (17.86) 53 (20.46) 1.82 0.004 21060 (17.87) 18 (16.22) 1.03 0.92

Male sex, n (%) 131395 (46.03) 194 (57.06) 1.56 < 0.0001 54098 (45.97) 88 (33.98) 0.61 0.0001 54134 (45.94) 52 (46.85) 1.04 0.85

Ethnicity (%) 0.0004 0.54 0.93

White 270360 (94.70) 307 (90.29) 1 REF 112010 (95.17) 248 (95.75) 1 REF 112153 (95.18) 105 (94.59) 1 REF

Black 4163 ( 1.46) 13 ( 3.82) 2.74 0.0004 1266 ( 1.08) 4 ( 1.54) 1.43 0.48 1269 ( 1.08) 1 ( 0.90) 0.84 0.86

South Asian 4701 ( 1.65) 7 ( 2.06) 1.31 0.48 2225 ( 1.89) 5 ( 1.93) 1.01 0.97 2228 ( 1.89) 2 ( 1.80) 0.96 0.95

Others 6253 ( 2.19) 13 ( 3.82) 1.83 0.03 2188 ( 1.86) 2 ( 0.77) 0.41 0.21 2187 ( 1.86) 3 ( 2.70) 1.46 0.51

Deprivation index (score) -1.40 (3.01) -0.31 (3.41) 1.37 < 0.0001 -1.44 (2.93) -1.11 (3.15) 1.12 0.07 -1.44 (2.93) -0.85 (3.30) 1.21 0.03

Current/former smoker, n (%) 126186 (44.20) 191 (56.18) 1.62 < 0.0001 52058 (44.23) 120 (46.33) 1.09 0.50 52126 (44.24) 52 (46.85) 1.11 0.58

Alcohol drinking status, n (%) 0.001 0.03 0.77

Never 11983 ( 4.20) 19 ( 5.59) 1 REF 5033 ( 4.28) 17 ( 6.56) 1 REF 5045 ( 4.28) 5 ( 4.50) 1 REF

Former 9259 ( 3.24) 22 ( 6.47) 1.5 0.20 3893 ( 3.31) 14 ( 5.41) 1.06 0.86 3902 ( 3.31) 5 ( 4.50) 1.29 0.68

Current 264235 (92.56) 299 (87.94) 0.71 0.15 108763 (92.42) 228 (88.03) 0.62 0.06 108890 (92.41) 101 (90.99) 0.94 0.89

BMI (kg/m2) 27.27 (4.60) 29.02 (5.25) 1.38 < 0.0001 27.38 (4.68) 28.43 (5.40) 1.23 0.0003 27.39 (4.68) 27.72 (5.00) 1.07 0.49

BMI categories, n (%) < 0.0001 0.16 0.18

Underweight 1245 ( 0.44) 1 ( 0.29) 1.05 0.96 490 ( 0.42) 1 ( 0.39) 1.01 0.99 491 ( 0.42) 0 ( 0.00) 0.00 0.98

Normal 95864 (33.58) 73 (21.47) 1 REF 38492 (32.71) 78 (30.12) 1 REF 38529 (32.70) 41 (36.94) 1 REF

Overweight 121857 (42.69) 150 (44.12) 1.62 0.0008 50404 (42.83) 102 (39.38) 1.00 0.99 50469 (42.83) 37 (33.33) 0.69 0.10

Obese 66511 (23.30) 116 (34.12) 2.29 < 0.0001 28303 (24.05) 78 (30.12) 1.36 0.06 28348 (24.06) 33 (29.73) 1.09 0.70

Body fat-free mass (Kg) 53.44 (11.46) 56.26 (11.40) 1.26 < 0.0001 53.44 (11.55) 51.62 (12.00) 0.85 0.01 53.44 (11.55) 53.56 (11.98) 1.01 0.88

Body fat mass (Kg) 24.48 ( 9.23) 27.21 (10.37) 1.3 < 0.0001 24.70 ( 9.43) 27.51 (10.96) 1.30† < 0.0001 24.71 ( 9.43) 24.93 (10.27) 1.03 0.78

Body fat proportion (percent) 31.10 (8.46) 32.21 (8.56) 1.14 0.02 31.29 (8.49) 34.27 (8.76) 1.41 < 0.0001 31.29 (8.49) 31.27 (9.03) 1.00 0.98

Systolic blood pressure (mmHg) 137.15 (18.14) 139.27 (18.57) 1.12 0.03 137.47 (18.38) 137.31 (17.63) 0.99 0.90 137.47 (18.38) 137.30 (18.41) 0.99 0.93

Diastolic blood pressure (mmHg) 82.01 ( 9.92) 83.53 (10.46) 1.16 0.005 82.13 (10.03) 81.15 ( 9.70) 0.91 0.12 82.13 (10.03) 82.36 (10.92) 1.03 0.79

FEV1(litres) 2.75 (0.77) 2.66 (0.73) 0.89 0.03 2.75 (0.79) 2.42 (0.81) 0.64† < 0.0001 2.75 (0.79) 2.66 (0.86) 0.89 0.21

FVC (litres) 3.64 (0.98) 3.56 (0.93) 0.92 0.15 3.64 (1.04) 3.26 (1.01) 0.66† < 0.0001 3.64 (1.04) 3.52 (1.03) 0.89 0.22

FEV1/FVC 0.76 (0.07) 0.75 (0.08) 0.9 0.04 0.76 (0.07) 0.74 (0.10) 0.84† 0.002 0.76 (0.07) 0.75 (0.08) 0.94 0.49

Walking pace (%) < 0.0001 < 0.0001 0.65

Slow 19101 ( 6.69) 54 (15.88) 2.40 < 0.0001 8026 ( 6.82) 35 (13.51) 1.90 0.0006 8051 ( 6.83) 10 ( 9.01) 1.33 0.40

Average 150594 (52.75) 177 (52.06) 1 REF 62121 (52.78) 142 (54.83) 1 REF 62205 (52.79) 58 (52.25) 1 REF

Brisk 115782 (40.56) 109 (32.06) 0.80 0.07 47542 (40.40) 82 (31.66) 0.75 0.04 47581 (40.38) 43 (38.74) 0.97 0.88

Grip strength (Kg) 30.85 (10.93) 31.21 (10.77) 1.03† 0.55 30.74 (11.04) 27.68 (10.77) 0.75 < 0.0001 30.74 (11.04) 30.62 (11.80) 0.98 0.87



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Prevalent disease at baseline (%) Longstanding illness, n (%) 83006 (29.08) 148 (43.53) 1.88 < 0.0001 35361 (30.05) 108 (41.70) 1.66 < 0.0001 35427 (30.06) 42 (37.84) 1.42 0.08

Diabetes 13626 ( 4.77) 32 ( 9.41) 2.07 < 0.0001 5665 ( 4.81) 25 ( 9.65) 2.11 0.0004 5684 ( 4.82) 6 ( 5.41) 1.13 0.78

CVD 13785 ( 4.83) 34 (10.00) 2.19 < 0.0001 5977 ( 5.08) 21 ( 8.11) 1.65 0.03 5994 ( 5.09) 4 ( 3.60) 0.70 0.48

Cancer 19368 ( 6.80) 31 ( 9.25) 1.4 0.08 8034 ( 6.85) 30 (11.58) 1.78 0.003 8052 ( 6.85) 12 (10.81) 1.65 0.10

Depression 54199 (18.99) 62 (18.24) 0.95 0.72 22173 (18.84) 36 (13.90) 0.70 0.04 22188 (18.83) 21 (18.92) 1.01 0.98

CKD stages 3-5 353 ( 0.12) 2 ( 0.59) 4.76 0.03 152 ( 0.13) 0 ( 0.00) 0.00 0.97 152 ( 0.13) 0 ( 0.00) 0.00 0.98

SLE 341 ( 0.12) 0 ( 0.00) 0 0.96 151 ( 0.13) 0 ( 0.00) 0.00 0.97 151 ( 0.13) 0 ( 0.00) 0.00 0.98

Asthma 31367 (10.99) 47 (13.82) 1.3 0.10 12986 (11.03) 38 (14.67) 1.39 0.06 13010 (11.04) 14 (12.61) 1.16 0.60

Sleep apnoea 864 ( 0.30) 4 ( 1.18) 3.91 0.007 383 ( 0.33) 0 ( 0.00) 0.00 0.97 383 ( 0.33) 0 ( 0.00) 0.00 0.97

COPD 608 ( 0.21) 2 ( 0.59) 2.77 0.15 271 ( 0.23) 1 ( 0.39) 1.68 0.61 271 ( 0.23) 1 ( 0.90) 3.93 0.17

Bronchitis 2920 ( 1.02) 8 ( 2.35) 2.33 0.02 1275 ( 1.08) 6 ( 2.32) 2.16 0.06 1277 ( 1.08) 4 ( 3.60) 3.40 0.02

Pneumonia 3535 ( 1.24) 5 ( 1.47) 1.19 0.70 1384 ( 1.18) 5 ( 1.93) 1.65 0.27 1388 ( 1.18) 1 ( 0.90) 0.76 0.79

Other respiratory disease 1488 ( 0.52) 3 ( 0.88) 1.7 0.36 609 ( 0.52) 1 ( 0.39) 0.75 0.77 610 ( 0.52) 0 ( 0.00) 0.00 0.97

Medication at baseline, n (%) Statin 44164 (15.47) 86 (25.29) 1.85 < 0.0001 18733 (15.92) 47 (18.15) 1.17 0.33 18770 (15.93) 10 ( 9.01) 0.52 0.050

BP medication 45493 (15.94) 96 (28.24) 2.07 < 0.0001 19078 (16.21) 42 (16.22) 1.00 1.00 19106 (16.21) 14 (12.61) 0.75 0.31

Steroid 1070 ( 0.37) 2 ( 0.59) 1.57 0.52 440 ( 0.37) 2 ( 0.77) 2.07 0.31 442 ( 0.38) 0 ( 0.00) 0.00 0.98

Biomarker at baseline Total cholesterol (mmol/L) 5.62 (4.91-6.33) 5.44 (4.62-6.20) 0.83† 0.0004 5.62 (4.90-6.34) 5.62 (4.84-6.28) 0.99 0.85 5.62 (4.90-6.34) 5.70 (4.96-6.29) 1.07 0.48

HDL cholesterol (mmol/L) 1.40 (1.18-1.68) 1.29 (1.07-1.53) 0.70 < 0.0001 1.40 (1.17-1.67) 1.41 (1.16-1.70) 1.00 0.94 1.40 (1.17-1.67) 1.36 (1.11-1.62) 0.87 0.15

Cystatin C (mg/L) 0.88 (0.80-0.97) 0.94 (0.85-1.04) 1.45 < 0.0001 0.88 (0.80-0.98) 0.92 (0.81-1.01) 1.20 0.001 0.88 (0.80-0.98) 0.91 (0.82-0.99) 1.09 0.36

HbA1c (mmol/mol) 35.10 (32.60-37.70) 36.30 (33.40-39.70) 1.28† < 0.0001 35.10 (32.60-37.70) 36.10 (33.70-38.95) 1.20 < 0.0001 35.10 (32.60-37.70) 35.20 (32.40-38.25) 1.10 0.22

CRP (mg/L) 1.26 (0.63-2.58) 1.68 (0.88-3.12) 1.15† 0.0006 1.27 (0.63-2.61) 1.51 (0.91-3.17) 1.14 0.003 1.27 (0.64-2.61) 1.46 (0.77-3.06) 1.12 0.12

Rheumatoid factor (IU/ml) 3.16 (3.16-3.16) 3.16 (3.16-3.16) 0.98 0.73 3.16 (3.16-3.16) 3.16 (3.16-3.16) 1.04 0.46 3.16 (3.16-3.16) 3.16 (3.16-3.16) 1.04 0.64

Red cell distribution width (%) 13.31 (12.90-13.81) 13.32 (12.98-13.95) 1.12 0.02 13.30 (12.89-13.80) 13.40 (12.96-14.07) 1.15 0.010 13.30 (12.89-13.80) 13.29 (12.98-13.79) 0.97 0.73

White cell count (10^9 cells/Litre) 6.60 (5.61-7.80) 6.85 (5.76-8.27) 1.20 0.0004 6.64 (5.65-7.81) 7.02 (5.78-8.42) 1.23 0.0004 6.64 (5.65-7.82) 6.60 (5.51-7.86) 1.04 0.64

Neutrophil count (10^9 cells/Litre) 4.00 (3.25-4.90) 4.12 (3.25-5.10) 1.11 0.051 4.01 (3.27-4.93) 4.18 (3.39-5.27) 1.14 0.02 4.01 (3.27-4.93) 3.98 (3.26-5.09) 1.01 0.92

Lymphocyte count (10^9 cells/Litre) 1.88 (1.52-2.29) 1.94 (1.55-2.43) 1.19† 0.0008 1.88 (1.52-2.30) 1.98 (1.62-2.44) 1.20 0.002 1.88 (1.52-2.30) 1.90 (1.54-2.52) 1.07† 0.47

Monocyte count (10^9 cells/Litre) 0.45 (0.36-0.56) 0.48 (0.38-0.60) 1.17 0.002 0.45 (0.36-0.56) 0.48 (0.38-0.60) 1.12 0.06 0.45 (0.36-0.57) 0.49 (0.40-0.57) 1.11† 0.27

Numbers represent number (%) for categorical variables, mean (SD) for gaussian continuous variables, and median (25-75th percentiles) for

skewed variables

*Univariable relative risk ratio per 1 standard deviation for continuous variables and using comparator group for categorical variables.

†Evidence for non-linearity (see supplementary material)



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Table 2. Association of risk factors for COVID-19 and pneumonia in UK Biobank

COVID-19 Pneumonia (after 2016)

Model 1 Model 2 Model 1 Model 2

RR (95% CI) P RR (95% CI) P RR (95% CI) P RR (95% CI) P

Age (per 5 years) 1.15 (1.08, 1.24) < 0.0001 1.10 (1.02, 1.36) 0.02 1.19 (1.10, 1.29) < 0.0001 1.10 (1.00, 1.20) 0.047

Male Sex 1.54 (1.24, 1.91) 0.0001 1.64 (1.25, 2.14) 0.0004 0.60 (0.46, 0.77) < 0.0001 0.82 (0.60, 1.13) 0.24

Ethnicity White 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) Black 2.07 (1.16, 3.71) 0.01 1.86 (1.03, 3.36) 0.04 1.43 (0.52, 3.89) 0.49 1.08 (0.39, 2.99) 0.88

South Asian 1.17 (0.55, 2.51) 0.68 1.12 (0.51, 2.43) 0.78 1.09 (0.45, 2.65) 0.85 0.77 (0.31, 1.93) 0.58

Others 1.65 (0.93, 2.91) 0.09 1.58 (0.89, 2.81) 0.12 0.43 (0.11, 1.76) 0.24 0.36 (0.09, 1.48) 0.16

Deprivation index (per 1 SD) 1.35 (1.23, 1.49) < 0.0001 1.26 (1.14, 1.40) < 0.0001 1.14 (1.01, 1.29) 0.03 1.08 (0.95, 1.22) 0.25

Current/former smoker 1.44 (1.15, 1.79) 0.001 1.38 (1.11, 1.73) 0.004 1.07 (0.83, 1.38) 0.59 1.02 (0.80, 1.32) 0.85

Alcohol drinking status Never 1 (Reference) - - 1 (Reference) - -

Former 1.48 (0.78, 2.79) 0.23 - - 1.12 (0.54, 2.30) 0.77 - -

Current 0.83 (0.50, 1.36) 0.45 - - 0.70 (0.42, 1.18) 0.18 - -

BMI (per 1 SD) 1.33 (1.21, 1.46) < 0.0001 1.24 (1.12, 1.38) < 0.0001 1.21 (1.09, 1.35) 0.0005 1.13 (1.00, 1.27) 0.04

BMI categories Underweight 1.09 (0.15, 8.01) 0.93 - - 0.91 (0.13, 6.58) 0.93 - -

Normal 1 (Reference) - - 1 (Reference) - -

Overweight 1.44 (1.08, 1.92) 0.01 - - 1.04 (0.77, 1.41) 0.78 - -

Obese 1.97 (1.46, 2.65) < 0.0001 - - 1.35 (0.98, 1.86) 0.06 - -

Body fat-free mass (per 1 SD) 1.31 (1.09, 1.58) 0.004 - - 1.29 (1.02, 1.62) 0.03 - -

Body fat mass (per 1 SD) 1.35 (1.22, 1.49) < 0.0001 - - 1.22 (1.09, 1.37) 0.0007 - -

Body fat percent (per 1 SD) 1.55 (1.33, 1.80) < 0.0001 - - 1.30 (1.10, 1.54) 0.002 - -

Systolic blood pressure (per 1 SD) 1.03 (0.92, 1.16) 0.60 - - 0.94 (0.82, 1.07) 0.33 - -

Diastolic blood pressure (per 1 SD) 1.12 (1.00, 1.24) 0.047 1.05 (0.94, 1.17) 0.39 0.93 (0.82, 1.06) 0.29 - -

FEV1 (per 1 SD) 0.82 (0.71, 0.95) 0.007 0.90 (0.77, 1.04) 0.14 0.69 (0.58, 0.83) < 0.0001 0.73 (0.61, 0.88) 0.0007

FVC (per 1 SD) 0.83 (0.72, 0.97) 0.02 - - 0.73 (0.61, 0.88) 0.0007 - -

FEV1/FVC (per 1 SD) 0.96 (0.87, 1.07) 0.45 - - 0.86 (0.77, 0.97) 0.01 - -

Walking pace Slow 2.04 (1.49, 2.79) < 0.0001 1.66 (1.20, 2.30) 0.002 1.74 (1.20, 2.53) 0.004 1.47 (0.99, 2.17) 0.054

Average 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference)



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Brisk 0.87 (0.68, 1.11) 0.26 1.00 (0.78, 1.29) 0.99 0.81 (0.61, 1.06) 0.12 0.91 (0.68, 1.20) 0.49

Grip strength (per 1 SD) 0.87 (0.74, 1.02) 0.09 0.92 (0.78, 1.08) 0.31 0.90 (0.73, 1.10) 0.31 1.02 (0.83, 1.26) 0.82

Prevalent disease at baseline Longstanding illness 1.65 (1.32, 2.06) < 0.0001 1.38 (1.09, 1.74) 0.007 1.58 (1.23, 2.03) 0.0004 1.36 (1.04, 1.77) 0.02

Diabetes 1.58 (1.09, 2.31) 0.02 1.20 (0.82, 1.77) 0.35 2.03 (1.33, 3.10) 0.0010 1.62 (1.05, 2.51) 0.03

CVD 1.66 (1.15, 2.40) 0.007 1.37 (0.94, 1.99) 0.10 1.52 (0.96, 2.41) 0.07 1.26 (0.79, 2.02) 0.33

Cancer 1.36 (0.93, 1.99) 0.11 1.34 (0.92, 1.96) 0.13 1.52 (1.03, 2.24) 0.03 1.51 (1.02, 2.22) 0.04

Depression 1.09 (0.82, 1.44) 0.55 1.15 (0.87, 1.52) 0.34 0.70 (0.49, 0.99) 0.045 0.74 (0.51, 1.05) 0.09

Asthma 1.33 (0.98, 1.82) 0.07 1.20 (0.88, 1.65) 0.25 1.38 (0.98, 1.96) 0.07 1.21 (0.85, 1.72) 0.29

COPD 2.14 (0.52, 8.76) 0.29 1.47 (0.36, 6.07) 0.59 1.37 (0.19, 9.85) 0.75 0.90 (0.12, 6.60) 0.92

Bronchitis 1.91 (0.93, 3.89) 0.08 1.52 (0.74, 3.12) 0.25 1.86 (0.82, 4.19) 0.14 1.46 (0.64, 3.35) 0.37

Pneumonia 1.12 (0.46, 2.74) 0.80 1.08 (0.44, 2.64) 0.86 1.58 (0.65, 3.85) 0.31 1.50 (0.61, 3.66) 0.37

Other respiratory disease 1.56 (0.49, 4.93) 0.45 1.42 (0.45, 4.49) 0.55 0.70 (0.10, 5.01) 0.72 0.64 (0.09, 4.62) 0.66

Medication at baseline Statin 1.47 (1.13, 1.91) 0.004 1.24 (0.95, 1.62) 0.12 1.04 (0.75, 1.45) 0.81 0.90 (0.64, 1.26) 0.53

BP medication 1.68 (1.30, 2.16) < 0.0001 1.40 (1.08, 1.82) 0.01 0.97 (0.69, 1.37) 0.86 0.84 (0.59, 1.19) 0.32

Steroid 1.46 (0.36, 5.95) 0.60 1.24 (0.31, 5.07) 0.76 1.94 (0.48, 7.82) 0.35 1.72 (0.42, 7.01) 0.45

Biomarker Total cholesterol (per 1 SD) 0.88 (0.79, 0.98) 0.02 0.90 (0.81, 1.00) 0.06 0.94 (0.83, 1.06) 0.33 0.97 (0.86, 1.10) 0.64

HDL cholesterol (per 1 SD) 0.74 (0.65, 0.84) < 0.0001 0.82 (0.72, 0.95) 0.006 0.89 (0.77, 1.02) 0.08 0.97 (0.84, 1.12) 0.71

Cystatin C (per 1 SD) 1.36 (1.23, 1.49) < 0.0001 1.24 (1.12, 1.37) < 0.0001 1.16 (1.03, 1.31) 0.01 1.06 (0.93, 1.21) 0.36

HbA1c (per 1 SD) 1.20 (1.11, 1.30) < 0.0001 1.11 (1.02, 1.21) 0.01 1.17 (1.06, 1.30) 0.001 1.10 (0.99, 1.23) 0.07

CRP (per 1 SD) 1.13 (1.04, 1.22) 0.004 1.04 (0.94, 1.14) 0.47 1.12 (1.02, 1.23) 0.02 1.04 (0.93, 1.16) 0.47

Rheumatoid factor (per 1 SD) 0.97 (0.87, 1.09) 0.67 0.97 (0.86, 1.09) 0.58 1.03 (0.92, 1.14) 0.63 1.02 (0.92, 1.14) 0.71

Red cell distribution width (per 1 SD) 1.09 (0.98, 1.20) 0.11 1.04 (0.94, 1.15) 0.49 1.12 (1.01, 1.25) 0.04 1.08 (0.97, 1.21) 0.18

White cell count (per 1 SD) 1.18 (1.06, 1.30) 0.002 1.08 (0.97, 1.20) 0.17 1.22 (1.09, 1.37) 0.0006 1.14 (1.01, 1.29) 0.03

Neutrophil count (per 1 SD) 1.10 (0.99, 1.22) 0.08 1.02 (0.91, 1.13) 0.75 1.15 (1.02, 1.29) 0.02 1.08 (0.96, 1.22) 0.22

Lymphocyte count (per 1 SD) 1.18 (1.06, 1.30) 0.002 1.10 (0.99, 1.22) 0.07 1.16 (1.04, 1.30) 0.01 1.11 (0.98, 1.25) 0.09

Monocyte count (per 1 SD) 1.10 (1.00, 1.23) 0.06 1.04 (0.93, 1.15) 0.49 1.16 (1.03, 1.31) 0.02 1.11 (0.98, 1.25) 0.11

Data presented as risk ratios and their 95% CI. continuous exposures were standardised and presented as 1-standar deviation increment. Variables with

associations denoted “-“ are excluded from the model due to non-significance in previous model and /or collinearity with another variable. Analyses were

adjusted for Model 1 (age, sex, ethnicity and deprivation) and model 2 (model 1 plus BMI, FEV1, and walking pace (and also DBP for COVID-19 only))



https://doi.org/10.1101/2020.04.28.20083295

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Table 3. Population attributable fractions of COVID-19 in the UK Biobank

COVID-19 Pneumonia after 2016

RR (95% CI) PAF RR (95% CI) PAF

Age 1.04 (0.95 to 1.13) 33.8% 1.10 (0.99 to 1.21) 59.0%

Current/former smoker 1.33 (1.06 to 1.67) 12.8% 0.98 (0.76 to 1.27) -0.8%

Male 1.27 (0.94 to 1.72) 11.2% 0.81 (0.57 to 1.15) -9.5%

Longstanding illness 1.27 (1.00 to 1.62) 7.3% 1.28 (0.97 to 1.69) 7.5%

BP medication 1.33 (1.02 to 1.73) 5.0% 0.79 (0.55 to 1.12) -3.6%

Walking pace: Slow 1.43 (1.02 to 1.99) 2.8% 1.27 (0.85 to 1.90) 1.9%

Deprivation index 1.23 (1.11 to 1.36) 2.2% 1.04 (0.92 to 1.19) 0.2%

Baseline cancer 1.28 (0.88 to 1.87) 1.9% 1.47 (0.99 to 2.17) 3.1%

Cystatin C 1.18 (1.06 to 1.31) 1.4% 1.04 (0.92 to 1.19) 0.1%

Ethnicity: Black 1.89 (1.01 to 3.53) 1.3% 1.16 (0.41 to 3.27) 0.4%

HDL cholesterol 0.87 (0.76 to 1.00) 0.9% 1.01 (0.87 to 1.17) 0.0%

HbA1c 1.12 (1.00 to 1.26) 0.7% 1.02 (0.89 to 1.17) 0.0%

BMI 1.11 (0.99 to 1.25) 0.6% 1.06 (0.93 to 1.21) 0.3%

FEV1 0.94 (0.81 to 1.09) 0.2% 0.77 (0.64 to 0.93) 3.5%

White cell count 1.01 (0.91 to 1.12) 0.0% 1.12 (0.99 to 1.27) 0.8%

Baseline diabetes 0.70 (0.42 to 1.17) -1.5% 1.39 (0.78 to 2.47) 1.8%

Estimated using prevalence in the study sample and RR shown in this table.

All factors shown are mutually adjusted.

RR shown in this table may be overadjusted, e.g. cystatin C and HDL cholesterol may be downstream factors of BMI.



https://doi.org/10.1101/2020.04.28.20083295

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Figure 1. Nonlinear associations of significant continuous variables with COVID-19 and pneumonia



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Figure 2. Risk factors for COVID-19 and Pneumonia in the UK Biobank cohort.

Data presented as Risk Ratios (RR) and their 95% CI. Analyses were adjusted for age, sex, ethnicity, deprivation, BMI, FEV1, and walking pace (and DBP for

COVID-19 only). Continuous exposures were standardised and presented 1-SD increment (deprivation index SD=3.01, Cystatin C SD=0.14, BMI SD=4.59,

HbA1c SD=5.80, white cell count SD=1.69, FEV1 SD=0.77 and HDL cholesterol SD=0.37).



https://doi.org/10.1101/2020.04.28.20083295

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